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Speeding Up Distributed Machine Learning Using Codes
Open accessAbstract
Codes are widely used in many engineering applications to offerrobustnessagainstnoise. In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms—straggler nodes, system failures, or communication bottlenecks—but there has been little interaction cutting across codes, machine learning, and distributed systems. In this paper, we provide theoretical insights on howcodedsolutions can achieve significant gains compared with uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms:matrix multiplicationanddata shuffling. For matrix multiplication, we use codes to alleviate the effect of stragglers and show that if the number of homogeneous workers is$n$, and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of$\log n$. For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction$\alpha $of the data matrix can be cached at each worker, and$n$is the number of workers,coded shufflingreduces the communication cost by a factor of$\left({\alpha + \frac {1}{n}}\right)\gamma (n)$compared with uncoded shuffling, where$\gamma (n)$is the ratio of the cost of unicasting$n$messages to$n$users to multicasting a common message (of the same size) to$n$users. For instance,$\gamma (n) \simeq n$if multicasting a message to$n$users is as cheap as unicasting a message to one user. We also provide experimental results, corroborating our theoretical gains of the coded algorithms.
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“Data poisoning attacks may occur in two ways such as clean-label attack (adversary can poison correct class of data sample) and dirty-label attack (adversary try to misclassify the target label of training dataset) [147], [148].”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [148]
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,905
Smith, D. K., & Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 12,385
(2010) · Cited by 4,411
Flagged first, then by citations
Research works printed but not linked: 3
- 10.1109/ISIT.2016.7541478 · DOI
- 2015, StarCluster
- 2015, Open MPI Open Source High Performance Computing
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Sorted by citations
Items 601–700 of 827
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Balancing on the Edge: Fast Service or No Service
Peng, P., & Soljanin, E. (2023) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct59356.2023.10419405 · Cited by 2
Energy Efficient Partial Distributed Coded Computing in Edge Computing
Li, Y., Zeng, D., Geng, H., & Yang, Z. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437571 · Cited by 2
Straggler-Resilient Differentially Private Decentralized Learning
Yakimenka, Y., Weng, C.-W., Lin, H.-Y., Rosnes, E., & Kliewer, J. (2024) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2024.3400995 · Cited by 3
Duality-Based Distributed Optimization With Communication Delays in Multicluster Networks
Wang, J., & Hu, G. (2024) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2024.3468403 · Cited by 2
Bu, L., Karpovsky, M. G., & Kinsy, M. A. (2018) · IET Computers & Digital Techniques · doi:10.1049/iet-cdt.2018.5008 · Cited by 2
A Computation vs Communication Tradeoff in Distributed Matrix Multiplication Over Finite Fields
Malladi, T. S., & Rajan, B. S. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761987 · Cited by 2
Capacity-achieving Polar-based LDGM Codes with Crowdsourcing Applications
Pang, J. C.-J., Mahdavifar, H., & Pradhan, S. S. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9174358 · Cited by 2
Computational Polarization: An Information-Theoretic Method for Resilient Computing
Pilancı, M. (2021) · IEEE Transactions on Information Theory · doi:10.1109/tit.2021.3139009 · Cited by 2
Analog Privacy-Preserving Coded Computing
Soleymani, M., Mahdavifar, H., & Avestimehr, A. S. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517715 · Cited by 2
Storage Codes With Flexible Number of Nodes
Li, W., Wang, Z., Lu, T., & Jafarkhani, H. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3222736 · Cited by 2
Optimizing Redundancy Levels in Master-Worker Compute Clusters for Straggler MitigationPreprint
Aktaş, M. F., & Soljanin, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05345 · Cited by 2
Coded Distributed Computing Schemes with Smaller Numbers of Input Files and Output FunctionsPreprint
Jiang, J., & Qu, L. (2020) · arXiv (Cornell University) · Cited by 2
Straggler-resistant distributed matrix computation via coding theoryPreprint
Ramamoorthy, A., Das, A. B., & Tang, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03515 · Cited by 2
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Chen, J., Wang, S., Carin, L., & Tao, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02949 · Cited by 2
Cheng, C., Dai, H., Li, L., Wang, J., & Gu, F. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-67537-0_12 · Cited by 1
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Capacity-Achieving Polar-Based Codes With Sparsity Constraints on the Generator Matrices
Pang, J. C.-J., Mahdavifar, H., & Pradhan, S. S. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3282603 · Cited by 1
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Li, T., He, X., Jin, R., & Dai, H. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3357857 · Cited by 1
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Wang, Y., Gu, S., Zhang, Z., Zhang, Q., & Xiang, W. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437556 · Cited by 1
Efficient Node Selection for Coding-based Timely Computation over Heterogeneous Systems
Lin, Y., Tang, B., Zhou, S., Xie, Z., & Ye, B. (2023) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00065 · Cited by 1
A coding computation scheme for secure aggregation
Yang, Z., & Tian, Y. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102073 · Cited by 1
Distributed Single-Source Shortest Path with Only Local Relaxation
Tang, J., Gong, S., Zhang, Y., Fu, C., & Yu, G. (2024) · Electronics · doi:10.3390/electronics13132502 · Cited by 1
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Wang, L., Hu, Y., Liu, Y., Xiao, R., & Feng, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-69766-1_33 · Cited by 1
Zhu, H., Chen, L., Wen, D., Chen, X., & Wang, W. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3450797 · Cited by 1
Sequence-aware Coding for Matrix Multiplication with Arbitrary Recoverability
Zou, Y., & Li, J. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622502 · Cited by 1
Ardakani, M. H., Ardakani, M., & Tellambura, C. (2024) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn61752.2024.10625058 · Cited by 1
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Li, Z., Huang, J., Zhang, T., Zhou, S., Wang, Q., Li, Y., Liu, J., Jiang, W., & Wang, J. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3581112 · Cited by 1
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Ramírez-Gordillo, T., Pujol, F. A., & Mora, H. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132828 · Cited by 1
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You, X., Wan, K., Zhang, X., Huang, W., Qiu, R. C., & Caire, G. (2026) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit62367.2026.11654063 · Cited by 1
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Phu, V. N., & Tran, V. T. N. (2018) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-02674-5_5 · Cited by 1
LT Codes with Feedback: Accelerate the Distributed Matrix-Vector Multiplication with Stragglers
Yang, X., Jiang, M., & Zhao, C. (2019) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc47392.2019.8958745 · Cited by 1
Natarajan, L. P., Krishnan, P., Lalitha, V., & Dau, S. H. (2020) · IEEE Transactions on Information Theory · doi:10.1109/tit.2020.3015516 · Cited by 1
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Krishnan, P., Natarajan, L. P., & Lalitha, V. (2021) · Entropy · doi:10.3390/e23080985 · Cited by 1
On the Capacity of Privacy-Preserving and Straggler-Robust Distributed Coded Computing
Zeng, Q., & Zhou, S. (2021) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc52777.2021.9580206 · Cited by 1
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Securely Straggler-Exploiting Coded Computation for Distributed Matrix Multiplication
Yang, H., Hong, S., & Lee, J. (2021) · IEEE Access · doi:10.1109/access.2021.3135581 · Cited by 1
Cheng, C., Li, L., Wang, J., & Gu, F. (2021) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00049 · Cited by 1
A High-Speed Codec Architecture for Lagrange Coded Computing
Xiong, B., Tian, J., & Wang, Z. (2022) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas48785.2022.9937664 · Cited by 1
Adaptive Privacy-Preserving Coded Computing with Hierarchical Task Partitioning
Zeng, Q., Nan, Z., & Zhou, S. (2024) · Entropy · doi:10.3390/e26100881 · Cited by 1
Speeding up distributed storage and computing systems using codes
Lee, K. W. (2016) · eScholarship (California Digital Library) · Cited by 1
Self-citationWireless MapReduce Distributed ComputingPreprint
Li, F., Chen, J., & Wang, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.00894 · Cited by 1
Index Codes with Minimum Locality for Three Receiver Unicast Problems
Joy, S. K., & Natarajan, L. P. (2020) · National Conference on Communications · doi:10.1109/ncc48643.2020.9055994 · Cited by 1
Metadata
- Authors
- Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., & Ramchandran, K.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- University of California, Berkeley (US)
- University of California, Santa Barbara (US)
- University of Wisconsin–Madison (US)
- Venue
- IEEE Transactions on Information Theory
- Published
- 2018-03
- Volume
- 64
- Issue
- 3
- Pages
- 1514-1529
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W2268702383
- Cited by
- 829
- References
- 122
- Citations by year
- 2026: 24 · 2025: 56 · 2024: 79 · 2023: 101 · 2022: 111 · 2021: 148 · 2020: 130 · 2019: 105 · 2018: 48
- FWCI
- 54.46
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Information Theory (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Institute for Information & communications Technology Promotion(IITP) grant
- Korea government(MSIT) (2017-0-00694)
- Brain Korea 21 Plus Project, and NSF CIF grant (1703678)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Data Storage Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- distributed learning, coded computing, straggler nodes, distributed matrix multiplication, data shuffling, communication bottleneck, multicast, caching
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